用most_fields策略，去实现cross-fields搜索，有3大弊端，而且搜索结果也显示出了这3大弊端，如何解决这些弊端呢？
第一个办法：用copy_to，将多个field组合成一个field
问题其实就出在有多个field，有多个field以后，我们只要想办法将一个标识跨在多个field的情况，合并成一个field即可。比如说，一个人名，本来是first_name，last_name，现在合并成一个full_name。
新增field映射new_author_first_name、new_author_first_name、new_author_full_name
PUT /forum/_mapping/article
{
"properties": {
"new_author_first_name": {
"type": "text",
"copy_to": "new_author_full_name"
},
"new_author_last_name": {
"type": "text",
"copy_to": "new_author_full_name"
},
"new_author_full_name": {
"type": "text"
}
}
}
用了这个copy_to语法之后，就可以将多个字段的值拷贝到一个字段中，并建立倒排索引
POST /forum/article/_bulk
{ "update": { "_id": "1"} }
{ "doc" : {"new_author_first_name" : "Peter", "new_author_last_name" : "Smith"} }
{ "update": { "_id": "2"} }
{ "doc" : {"new_author_first_name" : "Smith", "new_author_last_name" : "Williams"} }
{ "update": { "_id": "3"} }
{ "doc" : {"new_author_first_name" : "Jack", "new_author_last_name" : "Ma"} }
{ "update": { "_id": "4"} }
{ "doc" : {"new_author_first_name" : "Robbin", "new_author_last_name" : "Li"} }
{ "update": { "_id": "5"} }
{ "doc" : {"new_author_first_name" : "Tonny", "new_author_last_name" : "Peter Smith"} }
响应结果
{
"took": 20,
"errors": false,
"items": [
{
"update": {
"_index": "forum",
"_type": "article",
"_id": "1",
"_version": 9,
"result": "noop",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"status": 200
}
},
{
"update": {
"_index": "forum",
"_type": "article",
"_id": "2",
"_version": 13,
"result": "updated",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"status": 200
}
},
{
"update": {
"_index": "forum",
"_type": "article",
"_id": "3",
"_version": 9,
"result": "updated",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"status": 200
}
},
{
"update": {
"_index": "forum",
"_type": "article",
"_id": "4",
"_version": 13,
"result": "updated",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"status": 200
}
},
{
"update": {
"_index": "forum",
"_type": "article",
"_id": "5",
"_version": 8,
"result": "updated",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"status": 200
}
}
]
}
我们把new_author_first_name和new_author_last_name合并成为一个项new_author_full_name，但是这个region并不存在于我们文档的source里。当我们这么定义我们的mapping的话，在文档被索引之后，有一个新的new_author_full_name项可以供我们进行搜索。
如果想要显示new_author_full_name 设置映射时"store": true
PUT /forum/_mapping/article
{
"properties": {
"new_author_first_name": {
"type": "text",
"copy_to": "new_author_full_name"
},
"new_author_last_name": {
"type": "text",
"copy_to": "new_author_full_name"
},
"new_author_full_name": {
"type": "text",
"store": true
}
}
}
查询时
GET /forum/article/1?stored_fields=full_name
查询new_author_full_name包含Peter Smith的
GET /forum/article/_search
{
"query": {
"match": {
"new_author_full_name": {
"query": "Peter Smith",
"operator": "and"
}
}
}
}
相当于
GET /forum/article/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"new_author_full_name": "Peter"
}
},
{
"match": {
"new_author_full_name": "Smith"
}
}
]
}
}
}
响应结果
{
"took": 0,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 0.51623213,
"hits": [
{
"_index": "forum",
"_type": "article",
"_id": "1",
"_score": 0.51623213,
"_source": {
"articleID": "XHDK-A-1293-#fJ3",
"userID": 1,
"hidden": false,
"postDate": "2017-01-01",
"tag": [
"java",
"hadoop"
],
"tag_cnt": 2,
"view_cnt": 30,
"title": "this is java and elasticsearch blog",
"content": "i like to write best elasticsearch article",
"sub_title": "learning more courses",
"author_first_name": "Peter",
"author_last_name": "Smith",
"new_author_last_name": "Smith",
"new_author_first_name": "Peter"
}
},
{
"_index": "forum",
"_type": "article",
"_id": "5",
"_score": 0.5063205,
"_source": {
"articleID": "DHJK-B-1395-#Ky5",
"userID": 3,
"hidden": false,
"postDate": "2021-11-11",
"tag": [
"elasticsearch"
],
"tag_cnt": 1,
"view_cnt": 10,
"title": "this is spark blog",
"content": "spark is best big data solution based on scala ,an programming language similar to java",
"sub_title": "haha, hello world",
"author_first_name": "Tonny",
"author_last_name": "Peter Smith",
"new_author_last_name": "Peter Smith",
"new_author_first_name": "Tonny"
}
}
]
}
}
问题1：只是找到尽可能多的field匹配的doc，而不是某个field完全匹配的doc --> 解决，最完全匹配的document被最先返回
问题2：most_fields，没办法用minimum_should_match去掉长尾数据，就是匹配的特别少的结果 --> 解决，可以使用minimum_should_match去掉长尾数据
GET /forum/article/_search
{
"query": {
"bool": {
"should": [
{"match": {
"new_author_full_name": "Peter"
}},{
"match": {
"new_author_full_name": "Smith"
}
}
],
"minimum_should_match": 2 }
}
}
响应结果
{
"took": 0,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 0.51623213,
"hits": [
{
"_index": "forum",
"_type": "article",
"_id": "1",
"_score": 0.51623213,
"_source": {
"articleID": "XHDK-A-1293-#fJ3",
"userID": 1,
"hidden": false,
"postDate": "2017-01-01",
"tag": [
"java",
"hadoop"
],
"tag_cnt": 2,
"view_cnt": 30,
"title": "this is java and elasticsearch blog",
"content": "i like to write best elasticsearch article",
"sub_title": "learning more courses",
"author_first_name": "Peter",
"author_last_name": "Smith",
"new_author_last_name": "Smith",
"new_author_first_name": "Peter"
}
},
{
"_index": "forum",
"_type": "article",
"_id": "5",
"_score": 0.5063205,
"_source": {
"articleID": "DHJK-B-1395-#Ky5",
"userID": 3,
"hidden": false,
"postDate": "2021-11-11",
"tag": [
"elasticsearch"
],
"tag_cnt": 1,
"view_cnt": 10,
"title": "this is spark blog",
"content": "spark is best big data solution based on scala ,an programming language similar to java",
"sub_title": "haha, hello world",
"author_first_name": "Tonny",
"author_last_name": "Peter Smith",
"new_author_last_name": "Peter Smith",
"new_author_first_name": "Tonny"
}
}
]
}
}
问题3：TF/IDF算法，比如Peter Smith和Smith Williams，搜索Peter Smith的时候，由于first_name中很少有Smith的，所以query在所有document中的频率很低，得到的分数很高，可能Smith Williams反而会排在Peter Smith前面 --> 解决，Smith和Peter在一个field了，所以在所有document中出现的次数是均匀的，不会有极端的偏差
